Faster Thermal Profiling of a Lunar Rover with Machine Learning Adapted Finite Difference Model
arXiv:2605.27651v1 Announce Type: new Abstract: Autonomous space systems operating in extreme thermal environments require accurate and efficient thermal modeling to support both pre-mission system design and onboard autonomy. For lunar rovers, large temperature gradients, radiative heat transfer, and variable surface conditions make reliable thermal prediction especially challenging. High-fidelity physics-based simulations provide accurate results but are computationally expensive, while simplified models and lookup-table approach often lack sufficient accuracy. Physics-informed machine learn
The increasing push for autonomous space missions requires more efficient and accurate thermal modeling solutions to overcome computational limitations.
This development allows for faster and more reliable design and operation of space systems, particularly in extreme environments like the moon, by leveraging machine learning to enhance physics-based models.
Traditional computationally expensive thermal simulations are being augmented or replaced by AI-adapted models, leading to quicker iterations and potentially more robust mission planning.
- · Space agencies
- · Aerospace manufacturers
- · AI/ML research institutions
- · Lunar exploration programs
- · Traditional high-fidelity physics simulation software (without ML integration)
More efficient development cycles for lunar rovers and other space vehicles due to improved thermal profiling.
Reduced design errors and enhanced operational reliability of autonomous systems in extreme space environments.
Accelerated and more frequent deep-space missions as a result of lower technical hurdles and faster development of robust hardware.
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Read at arXiv cs.LG